code: def __call__(self, sample):
      img = sample["image"]
      img = (img * 2.0) / 255.0 - 1
      # print("Normalize:", img.shape, img.min(), img.max())
      # import ipdb; ipdb.set_trace()
      return {"image": img, "label": sample["label"]}
  
file_path: insightface/reconstruction/ostec/external/graphonomy/FaceHairMask/graphonomy_process.py
imports: import torch
  import torch.nn
  from torch.nn import functional
  from torchvision import transforms
label_desc: Use image node_id 1 for calling the Normalize_xception_tf_only_img obj's underlying member method code with example usage: obj.__call__(sample) and returns: dict